def __init__(self,
distance_matrix,
demand,
truck_capacity,
depot=0,
initial_solution=None,
initial_solution_strategy='random',
neighborhood='2-opt-star',
seed=None,
tabu_size=None):
CVRP.__init__(self, distance_matrix, demand, truck_capacity, depot,
initial_solution, initial_solution_strategy,
neighborhood, seed)
self.tabu_size = tabu_size
self.tabu = [self.current_solution_]
self.current_cost_ = self._evaluate_solution(self.current_solution_)
self.best_solution = self.current_solution_
self.best_cost = self.current_cost_
def get_solutions(self):
cvrps = {
"GUIDED_LOCAL_SEARCH": CVRP(
self.data, local_search_metaheuristic=routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH, time_limit=self.time_limit),
"TABU_SEARCH": CVRP(
self.data, local_search_metaheuristic=routing_enums_pb2.LocalSearchMetaheuristic.TABU_SEARCH, time_limit=self.time_limit),
"PATH_CHEAPEST_ARC": CVRP(
self.data, first_solution_strategy=routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
}
future_solutions = {}
for key, cvrp in cvrps.items():
future_solutions[key] = self.executor.submit(cvrp.get_solution)
solutions = {}
for key, future_solution in future_solutions.items():
solutions[key] = future_solution.result()
return solutions
def __init__(self,
distance_matrix,
demand,
truck_capacity,
depot=0,
initial_solution=None,
initial_solution_strategy='random',
neighborhood='2-opt-star',
seed=None,
t_max=100,
t_min=0.01,
cooling_function=(lambda (t, i): t * 0.99),
max_iterations_at_each_t=50):
CVRP.__init__(self, distance_matrix, demand, truck_capacity, depot,
initial_solution, initial_solution_strategy,
neighborhood, seed)
self.t_max = float(t_max)
self.t_min = float(t_min)
self.cooling_function = cooling_function
self.max_iterations_at_each_t = max_iterations_at_each_t
def cvrp():
mat, capacity, cities_nb, vehicules_nb, demand_matrix, coords = get_mat()
# CVRP
vrp = CVRP(vehicules_nb,cities_nb)
if mat is None:
vrp.create_data_model()
else:
vrp.pass_matrix(mat, demand_matrix,capacity)
print(vrp.data)
vrp_solve(vrp)
def call_stats_dev(arg1):
print('VRP ou CVRP : ', end='')
vrp_type = input()
if vrp_type.upper() != 'VRP' and vrp_type.upper() != 'CVRP':
raise Exception("Type de VRP non reconnu")
dataset_name = None
print('Chemin vers le fichier contenant les path : ', end='')
path = input()
if(os.path.isfile(path) == False):
raise Exception("Chemin invalide")
allPath = get_all_path(path)
for line in allPath:
dataset_name = os.path.basename(line[0])
mat, capacity, cities_nb, vehicules_nb, demand_matrix, coords = from_file_to_adj_matr(line[0])
if vrp_type.upper() == 'VRP':
# VRP
vrp = VRP(vehicules_nb,cities_nb)
vrp.pass_matrix(mat)
elif vrp_type.upper() == 'CVRP':
# CVRP
vrp = CVRP(vehicules_nb,cities_nb)
vrp.pass_matrix(mat, demand_matrix,capacity)
path_cost = line[1]
if(os.path.isfile(path_cost) == False):
raise Exception("Chemin invalide")
cost = get_particular_info(path_cost, 'cost')
print(line[0], line[1])
execution_quality_cities(algos_metaheuristic, vrp, cities_nb, dataset_name, cost)
def call_stats(arg1):
print('VRP ou CVRP : ', end='')
vrp_type = input()
if vrp_type.upper() != 'VRP' and vrp_type.upper() != 'CVRP':
raise Exception("Type de VRP non reconnu")
dataset_name = None
if arg1.upper()== "TIMECITIESQ":
random = False
else:
random = query_yes_no("Générer une matrice aléatoirement ?")
if not random:
print('Chemin vers le fichier : ', end='')
path = input()
if(os.path.isfile(path) == False):
raise Exception("Chemin invalide")
dataset_name = os.path.basename(path)
mat, capacity, cities_nb, vehicules_nb, demand_matrix, coords = from_file_to_adj_matr(path)
else:
print('Entrez le nombre de villes : ', end='')
cities_nb = int(input())
print('Entrez le nombre de véhicules : ', end='')
vehicules_nb = int(input())
dataset_name = "random-k" + str(vehicules_nb) + "-n" + str(cities_nb)
if vrp_type.upper() == 'VRP':
# VRP
vrp = VRP(vehicules_nb,cities_nb)
if random:
vrp.create_data_model()
else:
vrp.pass_matrix(mat)
elif vrp_type.upper() == 'CVRP':
# CVRP
vrp = CVRP(vehicules_nb,cities_nb)
if random:
vrp.create_data_model()
else:
vrp.pass_matrix(mat, demand_matrix,capacity)
if arg1.upper()== "TIMESOLUTIONS":
solutionsLimitArray = []
while True:
print('Appuyez sur enter pour arreter l ajout de solution... ')
print('Entrez un nombre dans le tableau de solutions locales max : ', end='')
tempLimit = input()
if tempLimit.isnumeric():
solutionsLimitArray.append(int(tempLimit))
else:
print("Fin de l'entrée, tableau : " , solutionsLimitArray)
break
execution_time_solutions(algos_metaheuristic, vrp, solutionsLimitArray, dataset_name)
elif arg1.upper()== "TIMEVEHICULES":
for i in tqdm(range(vehicules_nb)):
execution_time_vehicules(algos_metaheuristic, vrp, i, dataset_name)
elif arg1.upper()== "TIMECITIES":
for i in tqdm(range(cities_nb)):
execution_time_cities(algos_metaheuristic, vrp, i, dataset_name)
elif arg1.upper()== "TIMECITIESQ":
print('Chemin vers le fichier de la solution: ', end='')
path_cost = input()
if(os.path.isfile(path_cost) == False):
raise Exception("Chemin invalide")
cost = get_particular_info(path_cost, 'cost')
execution_quality_cities(algos_metaheuristic, vrp, cities_nb, dataset_name, cost)
print('Appuyez sur entrée pour continuer...')
input()
from vrp import VRP
from cvrp import CVRP
filename = 'PL'
if __name__ == '__main__':
print("\nVRP: ")
vrp = VRP(filename)
vrp.calculate_best_base_location()
print("\nCVRP: ")
cvrp = CVRP(filename)
cvrp.calculate_best_base_location()